It is foreseeable that wireless communications in the future may require a large amount of UEs to be served simultaneously. For the scenario of Internet of Things (IoT) for example, the volume of UEs (UE equipment) is expected to grow 10 to 100 times. Although the IoT would offer great challenges of creating a world in which all things around us, known as smart objects, are inter-connected by a wireless communication system, the broad vision of IoT nevertheless has revealed its great potential to improve the qualities of lives. However, a substantial amount of challenges, such as battery power consumptions, interferences among a large amount of UEs, costs of UEs, and so forth, would still need to be addressed.
Presently, communication systems are predominantly broadband communications. However, broadband communications over channels with large delay spreads could be a challenging task due to severe inter-symbol-interferences (ISI). To resolve this challenge, multicarrier modulations such as OFDM and complicated equalization could be needed at the receiver to mitigate any potential ISI. Although the performance might still be well enough by using OFDM, the consequence of using such measure would be high calculation complexities for typical UEs.
The concept of time-reversal division multiple access (TRDMA) was recently introduced as another multiple access scheme for broadband communication systems. In one application of TRDMA downlink system, a base station (BS) may simultaneously transmit data stream to every UE over different multipath channels. To exploit this spatial degrees of freedom of the channels, the BS will first time-reverse the channel impulse response (CR) of each UE's channel as the UE's signature waveform and then embed these signatures into corresponding data streams. When such a combined signal propagates to a target UE through corresponding multipath channels, the combined signal may end up with a “spiky” signal-power spatial distribution focused only at a corresponding UE. In such case, the receiver may only need to make the decision on the spiky signal power time sample. It has been shown that the system performance has both an effective signal-to-interference-plus-noise ratio (SINR) as well as achievable sum rate, and thus TRDMA would appear to be a promising candidate for future broadband wireless communications.
A comparative study between TRDMA and OFDM was made in related works by comparing the two broadband technologies in terms of computational complexities and achievable rates. It has been shown that TR (time-reversal) system may only need some adders at base station; whereas OFDM would need some multipliers because of FFT blocks. Furthermore, computational complexities of a TR system at the receiver side could be negligible since only one-tap detection is performed. This means that the overall computational complexity of TR system would appear much lower than an OFDM system.
However, the key point of TRDMA is that a base station should know the channel impulse response of each UE's channel as the signature waveform in order to time-reverse. This scheme could be very sensitive to channel estimation errors. When a channel estimation error is very large, it may cause a mismatch between a TR and the corresponding channel. Thus, a TRDMA based communication system could be different from the other communication systems since a TRDMA based communication system would require channel state information in time domain rather than frequency domain.
When UEs transmit data to base station, each of the UEs may need to perform the channel estimation. In some IoT scenarios, it should be noted that there could a lot of UEs requiring channel estimations simultaneously. The traditional channel estimation method is that UEs would perform the channel estimations one by one. This means that a UE can not perform a channel estimation as long as there is another UE which is current performing channel estimate. In this way, the UEs must wait for others to finish channel estimations. Thus, the UE will require a lot of time performing channel estimations when the amount of UEs is very large. Further, if the UEs perform channel estimations simultaneously by transmitting training sequences to base station, the training sequence sent by multiple UEs may interfere among one another in the training stage. In other words, one of the most pressing issues currently is the development of an efficient and well channel estimation method for large amount of UEs.